Modeling and Forecasting Neonatal Mortality in Ethiopia: A Comparative Study Using Statistical, Machine Learning, and Deep Learning Approaches

Modeling and Forecasting Neonatal Mortality in Ethiopia: A Comparative Study Using Statistical, Machine Learning, and Deep Learning Approaches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling and Forecasting Neonatal Mortality in Ethiopia: A Comparative Study Using Statistical, Machine Learning, and Deep Learning Approaches Abraham Keffale Mengistu, Muluken Belachew Mengistie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7209010/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Ethiopia faces alarming stagnation in neonatal mortality rates (NMR) at ~27 deaths/1,000 live births, jeopardizing Sustainable Development Goal (SDG) 3.2 targets. Predictive analytics using advanced computational approaches remains underutilized for guiding interventions in low-resource settings. Methods: This comparative study employed national-level NMR data (1977–2023) from the WHO Global Health Observatory to forecast trends through 2034. Five models were evaluated: Auto Regressive Integrated Moving Average (ARIMA), Prophet, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Data underwent logarithmic transformation and differencing to achieve stationarity. Model performance was assessed using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R² under walk-forward validation. Results: The LSTM model demonstrated superior accuracy (RMSE: 0.0009; MAPE: 2.93%), outperforming statistical and machine learning approaches. Forecasts indicate marginal NMR decline to 27.71 (2030) and 27.13 (2034) deaths/1,000 live births, far exceeding the SDG 3.2 target of ≤12. Structural barriers include rural healthcare access gaps (80% population coverage), substandard perinatal care (20% facility compliance), and diagnostic delays contributing to 50% of deaths. Conclusion: Ethiopia requires a tenfold acceleration in NMR reduction to meet 2030 targets. Integrating LSTM-based forecasting into national health systems could enable proactive resource allocation. Urgent scale-up of Kangaroo Mother Care, emergency transport financing, and perinatal quality improvement is recommended. This study establishes LSTM as a transformative tool for neonatal mortality prediction in resource-limited contexts. Neonatal mortality prediction Forecasting models Ethiopia Sustainable Development Goal 3.2 machine learning Public health informatics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7209010","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490549113,"identity":"d598c591-a970-47d1-864e-b420a0c2cf9e","order_by":0,"name":"Abraham Keffale Mengistu","email":"data:image/png;base64,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","orcid":"","institution":"Debre Markos University","correspondingAuthor":true,"prefix":"","firstName":"Abraham","middleName":"Keffale","lastName":"Mengistu","suffix":""},{"id":490549114,"identity":"cd5f2459-6031-4780-a866-5e9484898abc","order_by":1,"name":"Muluken Belachew Mengistie","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Muluken","middleName":"Belachew","lastName":"Mengistie","suffix":""}],"badges":[],"createdAt":"2025-07-24 23:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7209010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7209010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94062796,"identity":"f8093dbb-c3b4-462e-af25-1e4d2f8af5bc","added_by":"auto","created_at":"2025-10-22 07:16:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":743336,"visible":true,"origin":"","legend":"","description":"","filename":"Neonataltimeseries.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7209010/v1_covered_305a1838-4610-4b8d-979b-ebc13b09c93b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling and Forecasting Neonatal Mortality in Ethiopia: A Comparative Study Using Statistical, Machine Learning, and Deep Learning Approaches","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonatal mortality prediction, Forecasting models, Ethiopia, Sustainable Development Goal 3.2, machine learning, Public health informatics ","lastPublishedDoi":"10.21203/rs.3.rs-7209010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7209010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Ethiopia faces alarming stagnation in neonatal mortality rates (NMR) at ~27 deaths/1,000 live births, jeopardizing Sustainable Development Goal (SDG) 3.2 targets. Predictive analytics using advanced computational approaches remains underutilized for guiding interventions in low-resource settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This comparative study employed national-level NMR data (1977–2023) from the WHO Global Health Observatory to forecast trends through 2034. Five models were evaluated: Auto Regressive Integrated Moving Average (ARIMA), Prophet, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Data underwent logarithmic transformation and differencing to achieve stationarity. Model performance was assessed using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R² under walk-forward validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The LSTM model demonstrated superior accuracy (RMSE: 0.0009; MAPE: 2.93%), outperforming statistical and machine learning approaches. Forecasts indicate marginal NMR decline to 27.71 (2030) and 27.13 (2034) deaths/1,000 live births, far exceeding the SDG 3.2 target of ≤12. Structural barriers include rural healthcare access gaps (80% population coverage), substandard perinatal care (20% facility compliance), and diagnostic delays contributing to 50% of deaths.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Ethiopia requires a tenfold acceleration in NMR reduction to meet 2030 targets. Integrating LSTM-based forecasting into national health systems could enable proactive resource allocation. Urgent scale-up of Kangaroo Mother Care, emergency transport financing, and perinatal quality improvement is recommended. This study establishes LSTM as a transformative tool for neonatal mortality prediction in resource-limited contexts.\u003c/p\u003e","manuscriptTitle":"Modeling and Forecasting Neonatal Mortality in Ethiopia: A Comparative Study Using Statistical, Machine Learning, and Deep Learning Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 19:13:22","doi":"10.21203/rs.3.rs-7209010/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1257c326-fd37-428d-88b2-965de22849f5","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T20:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 19:13:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7209010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7209010","identity":"rs-7209010","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}